Model Deployment : Classifying Brain Tumors from Magnetic Resonance Images by Leveraging Convolutional Neural Network-Based Multilevel Feature Extraction and Hierarchical Representation¶

John Pauline Pineda

December 14, 2024

  • 1. Table of Contents
    • 1.1 Data Background
    • 1.2 Data Description
    • 1.3 Data Quality Assessment
    • 1.4 Data Preprocessing
    • 1.5 Data Exploration
      • 1.5.1 Exploratory Data Analysis
      • 1.5.2 Hypothesis Testing
    • 1.6 Predictive Model Development
      • 1.6.1 Pre-Modelling Data Preparation
      • 1.6.2 Data Splitting
      • 1.6.3 Convolutional Neural Network Sequential Layer Development
        • 1.6.3.1 CNN With No Regularization
        • 1.6.3.2 CNN With Dropout Regularization
        • 1.6.3.3 CNN With Batch Normalization Regularization
        • 1.6.3.4 CNN With Dropout and Batch Normalization Regularization
      • 1.6.4 CNN With No Regularization Model Fitting | Hyperparameter Tuning | Validation
      • 1.6.5 CNN With Dropout Regularization Model Fitting | Hyperparameter Tuning | Validation
      • 1.6.6 CNN With Batch Normalization Regularization Model Fitting | Hyperparameter Tuning | Validation
      • 1.6.7 CNN With Dropout and Batch Normalization Regularization Model Fitting | Hyperparameter Tuning | Validation
      • 1.6.8 Model Selection
      • 1.6.9 Model Testing
      • 1.6.10 Model Inference
    • 1.7 Predictive Model Deployment Using Streamlit and Streamlit Community Cloud
      • 1.7.1 Model Application Programming Interface Code Development
      • 1.7.2 User Interface Application Code Development
      • 1.7.3 Web Application
  • 2. Summary
  • 3. References

1. Table of Contents ¶

1.1 Data Background ¶

1.2 Data Description ¶

In [1]:
##################################
# Loading Python Libraries
##################################

##################################
# Data Loading, Data Preprocessing
# and Exploratory Data Analysis
##################################
import numpy as np
import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.cm as cm
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
%matplotlib inline

import tensorflow as tf
import keras

from PIL import Image
from glob import glob
import cv2
import os
import random
import math

##################################
# Model Development
##################################
from keras import backend as K
from keras import regularizers
from keras.models import Sequential, Model,load_model
from keras.layers import Input, Activation, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, MaxPool2D, AveragePooling2D, GlobalMaxPooling2D, BatchNormalization
from keras.optimizers import Adam, SGD
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import img_to_array, array_to_img, load_img
from math import ceil

##################################
# Model Evaluation
##################################
from keras.metrics import PrecisionAtRecall, Recall 
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
In [2]:
##################################
# Setting random seed options
# for the analysis
##################################
def set_seed(seed=123):
    np.random.seed(seed) 
    tf.random.set_seed(seed) 
    keras.utils.set_random_seed(seed)
    random.seed(seed)
    tf.config.experimental.enable_op_determinism()
    os.environ['TF_DETERMINISTIC_OPS'] = "1"
    os.environ['TF_CUDNN_DETERMINISM'] = "1"
    os.environ['PYTHONHASHSEED'] = str(seed)
set_seed()
In [3]:
##################################
# Filtering out unncessary warnings
##################################
import warnings
warnings.filterwarnings('ignore')
In [4]:
##################################
# Defining file paths
##################################
DATASETS_ORIGINAL_PATH = r"datasets\Brain_Tumor_MRI_Dataset"
DATASETS_FINAL_TRAIN_PATH = r"datasets\Brain_Tumor_MRI_Dataset\Training"
DATASETS_FINAL_TEST_PATH = r"datasets\Brain_Tumor_MRI_Dataset\Testing"
MODELS_PATH = r"models"
PARAMETERS_PATH = r"parameters"
PIPELINES_PATH = r"pipelines"
In [5]:
##################################
# Defining the image category levels
##################################
diagnosis_code_dictionary = {'Tr-no': 0,
                             'Tr-noTr': 0,
                             'Tr-gl': 1,
                             'Tr-glTr': 1,
                             'Tr-me': 2,
                             'Tr-meTr': 2,
                             'Tr-pi': 3,
                             'Tr-piTr': 3}

##################################
# Defining the image category descriptions
##################################
diagnosis_description_dictionary = {'Tr-no': 'No Tumor',
                                    'Tr-noTr': 'No Tumor',
                                    'Tr-gl': 'Glioma',
                                    'Tr-glTr': 'Glioma',
                                    'Tr-me': 'Meningioma',
                                    'Tr-meTr': 'Meningioma',
                                    'Tr-pi': 'Pituitary',
                                    'Tr-piTr': 'Pituitary'}

##################################
# Consolidating the image path
##################################
imageid_path_dictionary = {os.path.splitext(os.path.basename(x))[0]: x for x in glob(os.path.join("..", DATASETS_FINAL_TRAIN_PATH, '*','*.jpg'))}
In [6]:
##################################
# Taking a snapshot of the dictionary
##################################
dict(list(imageid_path_dictionary.items())[0:5])
Out[6]:
{'Tr-glTr_0000': '..\\datasets\\Brain_Tumor_MRI_Dataset\\Training\\glioma\\Tr-glTr_0000.jpg',
 'Tr-glTr_0001': '..\\datasets\\Brain_Tumor_MRI_Dataset\\Training\\glioma\\Tr-glTr_0001.jpg',
 'Tr-glTr_0002': '..\\datasets\\Brain_Tumor_MRI_Dataset\\Training\\glioma\\Tr-glTr_0002.jpg',
 'Tr-glTr_0003': '..\\datasets\\Brain_Tumor_MRI_Dataset\\Training\\glioma\\Tr-glTr_0003.jpg',
 'Tr-glTr_0004': '..\\datasets\\Brain_Tumor_MRI_Dataset\\Training\\glioma\\Tr-glTr_0004.jpg'}
In [7]:
##################################
# Consolidating the information
# from the dataset
# into a dataframe
##################################
mri_images = pd.DataFrame.from_dict(imageid_path_dictionary, orient = 'index').reset_index()
mri_images.columns = ['Image_ID','Path']
classes = mri_images.Image_ID.str.split('_').str[0]
mri_images['Diagnosis'] = classes
mri_images['Target'] = mri_images['Diagnosis'].map(diagnosis_code_dictionary.get) 
mri_images['Class'] = mri_images['Diagnosis'].map(diagnosis_description_dictionary.get)
In [8]:
##################################
# Performing a general exploration of the dataset
##################################
print('Dataset Dimensions: ')
display(mri_images.shape)

Dataset Dimensions: 

(5712, 5)
In [9]:
##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(mri_images.dtypes)

Column Names and Data Types:

Image_ID     object
Path         object
Diagnosis    object
Target        int64
Class        object
dtype: object
In [10]:
##################################
# Taking a snapshot of the dataset
##################################
mri_images.head()
Out[10]:
Image_ID Path Diagnosis Target Class
0 Tr-glTr_0000 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma
1 Tr-glTr_0001 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma
2 Tr-glTr_0002 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma
3 Tr-glTr_0003 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma
4 Tr-glTr_0004 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma
In [11]:
##################################
# Performing a general exploration of the numeric variables
##################################
print('Numeric Variable Summary:')
display(mri_images.describe(include='number').transpose())

Numeric Variable Summary:
count mean std min 25% 50% 75% max
Target 5712.0 1.465336 1.147892 0.0 0.0 1.0 3.0 3.0
In [12]:
##################################
# Performing a general exploration of the object variable
##################################
print('Object Variable Summary:')
display(mri_images.describe(include='object').transpose())

Object Variable Summary:
count unique top freq
Image_ID 5712 5712 Tr-pi_1440 1
Path 5712 5712 ..\datasets\Brain_Tumor_MRI_Dataset\Training\p... 1
Diagnosis 5712 8 Tr-no 1585
Class 5712 4 No Tumor 1595
In [13]:
##################################
# Performing a general exploration of the target variable
##################################
mri_images.Class.value_counts()
Out[13]:
Class
No Tumor      1595
Pituitary     1457
Meningioma    1339
Glioma        1321
Name: count, dtype: int64
In [14]:
##################################
# Performing a general exploration of the target variable
##################################
mri_images.Class.value_counts(normalize=True)
Out[14]:
Class
No Tumor      0.279237
Pituitary     0.255077
Meningioma    0.234419
Glioma        0.231268
Name: proportion, dtype: float64

1.3 Data Quality Assessment ¶

In [15]:
##################################
# Counting the number of duplicated images
##################################
mri_images.duplicated().sum()
Out[15]:
np.int64(0)
In [16]:
##################################
# Gathering the number of null images
##################################
mri_images.isnull().sum()
Out[16]:
Image_ID     0
Path         0
Diagnosis    0
Target       0
Class        0
dtype: int64

1.4 Data Preprocessing ¶

In [17]:
##################################
# Including the pixel information
# of the actual images
# in array format
# into a dataframe
##################################
mri_images['Image'] = mri_images['Path'].map(lambda x: np.asarray(Image.open(x).resize((200,200))))
In [18]:
##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(mri_images.dtypes)

Column Names and Data Types:

Image_ID     object
Path         object
Diagnosis    object
Target        int64
Class        object
Image        object
dtype: object
In [19]:
##################################
# Taking a snapshot of the dataset
##################################
mri_images.head()
Out[19]:
Image_ID Path Diagnosis Target Class Image
0 Tr-glTr_0000 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
1 Tr-glTr_0001 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
2 Tr-glTr_0002 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
3 Tr-glTr_0003 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
4 Tr-glTr_0004 ..\datasets\Brain_Tumor_MRI_Dataset\Training\g... Tr-glTr 1 Glioma [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...
In [20]:
##################################
# Taking a snapshot of the dataset
##################################
n_samples = 5
fig, m_axs = plt.subplots(4, n_samples, figsize = (2*n_samples, 10))
for n_axs, (type_name, type_rows) in zip(m_axs, mri_images.sort_values(['Class']).groupby('Class')):
    n_axs[2].set_title(type_name, fontsize = 14, weight = 'bold')
    for c_ax, (_, c_row) in zip(n_axs, type_rows.sample(n_samples, random_state=123).iterrows()):       
        picture = c_row['Path']
        image = cv2.imread(picture)
        resized_image = cv2.resize(image, (500,500))
        c_ax.imshow(resized_image)
        c_ax.axis('off')
No description has been provided for this image
In [21]:
##################################
# Sampling a single image
##################################
samples, features = mri_images.shape
plt.figure()
pic_id = random.randrange(0, samples)
picture = mri_images['Path'][pic_id]
image = cv2.imread(picture) 

<Figure size 640x480 with 0 Axes>
In [22]:
##################################
# Plotting using subplots
##################################
plt.figure(figsize=(15, 5))

##################################
# Formulating the original image
##################################
plt.subplot(1, 4, 1)
plt.imshow(image)
plt.title('Original Image', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the blue channel
##################################
plt.subplot(1, 4, 2)
plt.imshow(image[ : , : , 0])
plt.title('Blue Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the green channel
##################################
plt.subplot(1, 4, 3)
plt.imshow(image[ : , : , 1])
plt.title('Green Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Formulating the red channel
##################################
plt.subplot(1, 4, 4)
plt.imshow(image[ : , : , 2])
plt.title('Red Channel', fontsize = 14, weight = 'bold')
plt.axis('off')

##################################
# Consolidating all images
##################################
plt.show()
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In [23]:
##################################
# Determining the image shape
##################################
print('Image Shape:')
display(image.shape)

Image Shape:

(512, 512, 3)
In [24]:
##################################
# Determining the image height
##################################
print('Image Height:')
display(image.shape[0])

Image Height:

512
In [25]:
##################################
# Determining the image width
##################################
print('Image Width:')
display(image.shape[1])

Image Width:

512
In [26]:
##################################
# Determining the image dimension
##################################
print('Image Dimension:')
display(image.ndim)

Image Dimension:

3
In [27]:
##################################
# Determining the image size
##################################
print('Image Size:')
display(image.size)

Image Size:

786432
In [28]:
##################################
# Determining the image data type
##################################
print('Image Data Type:')
display(image.dtype)

Image Data Type:

dtype('uint8')
In [29]:
##################################
# Determining the maximum RGB value
##################################
print('Image Maximum RGB:')
display(image.max())

Image Maximum RGB:

np.uint8(255)
In [30]:
##################################
# Determining the minimum RGB value
##################################
print('Image Minimum RGB:')
display(image.min())

Image Minimum RGB:

np.uint8(0)
In [31]:
##################################
# Identifying the path for the images
# and defining image categories 
##################################
path = (os.path.join("..", DATASETS_FINAL_TRAIN_PATH))
classes=["notumor", "glioma", "meningioma", "pituitary"]
num_classes = len(classes)
batch_size = 32
In [32]:
##################################
# Creating subsets of images
# for model training and
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=2,
                                   width_shift_range=0.02,
                                   height_shift_range=0.02,
                                   horizontal_flip=False,
                                   vertical_flip=False,
                                   shear_range=0.02,
                                   zoom_range=0.02,
                                   validation_split=0.2)

##################################
# Loading the model training images
##################################
train_gen = train_datagen.flow_from_directory(directory=path, 
                                              target_size=(227, 227),
                                              class_mode='categorical',
                                              subset='training',
                                              shuffle=True, 
                                              classes=classes,
                                              batch_size=batch_size, 
                                              color_mode="grayscale")

Found 4571 images belonging to 4 classes.
In [33]:
##################################
# Loading samples of augmented images
# for the training set
##################################
##################################
# Loading samples of augmented images
# for the training set
##################################
fig, axes = plt.subplots(1, 5, figsize=(15, 3))

for i in range(5):
    batch = next(train_gen)
    images, labels = batch
    axes[i].imshow(images[0]) 
    axes[i].set_title(f"Label: {labels[0]}")
    axes[i].axis('off')
plt.show()
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In [34]:
##################################
# Creating subsets of images
# for model validation
# setting the parameters for
# real-time data augmentation
# at each epoch
##################################
set_seed()
val_datagen = ImageDataGenerator(rescale=1./255, 
                                 validation_split=0.2)

##################################
# Loading the model evaluation images
##################################
val_gen = val_datagen.flow_from_directory(directory=path, 
                                            target_size=(227, 227),
                                            class_mode='categorical',
                                            subset='validation',
                                            shuffle=False, 
                                            classes=classes,
                                            batch_size=batch_size, 
                                            color_mode="grayscale")

Found 1141 images belonging to 4 classes.
In [35]:
##################################
# Loading samples of original images
# for the validation set
##################################
images, labels = next(val_gen)
fig, axes = plt.subplots(1, 5, figsize=(15, 3))
for i, idx in enumerate(range(0, 5)):
    axes[i].imshow(images[idx])
    axes[i].set_title(f"Label: {labels[0]}")
    axes[i].axis('off')
plt.show()
No description has been provided for this image

1.5 Data Exploration ¶

1.5.1 Exploratory Data Analysis ¶

In [36]:
##################################
# Consolidating summary statistics
# for the image pixel values
##################################
mean_val = []
std_dev_val = []
max_val = []
min_val = []

for i in range(0, samples):
    mean_val.append(mri_images['Image'][i].mean())
    std_dev_val.append(np.std(mri_images['Image'][i]))
    max_val.append(mri_images['Image'][i].max())
    min_val.append(mri_images['Image'][i].min())

imageEDA = mri_images.loc[:,['Image', 'Class','Path']]
imageEDA['Mean'] = mean_val
imageEDA['StDev'] = std_dev_val
imageEDA['Max'] = max_val
imageEDA['Min'] = min_val
In [37]:
##################################
# Consolidating the overall mean
# for the pixel intensity means
# grouped by categories
##################################
imageEDA.groupby(['Class'])['Mean'].mean()
Out[37]:
Class
Glioma        32.716871
Meningioma    43.487954
No Tumor      60.815724
Pituitary     49.273456
Name: Mean, dtype: float64
In [38]:
##################################
# Consolidating the overall minimum
# for the pixel intensity means
# grouped by categories
##################################
imageEDA.groupby(['Class'])['Mean'].min()
Out[38]:
Class
Glioma        13.701850
Meningioma    18.233400
No Tumor       9.770775
Pituitary     24.699575
Name: Mean, dtype: float64
In [39]:
##################################
# Consolidating the overall maximum
# for the pixel intensity means
# grouped by categories
##################################
imageEDA.groupby(['Class'])['Mean'].max()
Out[39]:
Class
Glioma         68.372425
Meningioma    137.765375
No Tumor      125.066725
Pituitary     102.007950
Name: Mean, dtype: float64
In [40]:
##################################
# Consolidating the overall standard deviation
# for the pixel intensity means
# grouped by categories
##################################
imageEDA.groupby(['Class'])['Mean'].std()
Out[40]:
Class
Glioma         8.565834
Meningioma    14.307165
No Tumor      21.338225
Pituitary      8.222902
Name: Mean, dtype: float64
In [41]:
##################################
# Formulating the mean distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Mean', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Intensity Mean Distribution by Category', fontsize=14, weight='bold');
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In [42]:
##################################
# Formulating the maximum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Max', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Intensity Maximum Distribution by Category', fontsize=14, weight='bold');
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In [43]:
##################################
# Formulating the minimum distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'Min', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Intensity Minimum Distribution by Category', fontsize=14, weight='bold');
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In [44]:
##################################
# Formulating the standard deviation distribution
# by category of the image pixel values
##################################
sns.displot(data = imageEDA, x = 'StDev', kind="kde", hue = 'Class', height=6, aspect=1.40)
plt.title('Image Pixel Intensity Standard Deviation Distribution by Category', fontsize=14, weight='bold');
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In [45]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# by category of the image pixel values
##################################
plt.figure(figsize=(10,6))
sns.set(style="ticks", font_scale = 1)
ax = sns.scatterplot(data=imageEDA, x="Mean", y=imageEDA['StDev'], hue='Class', alpha=0.5)
sns.despine(top=True, right=True, left=False, bottom=False)
plt.xticks(rotation=0, fontsize = 12)
ax.set_xlabel('Image Pixel Intensity Mean',fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
plt.title('Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize = 14, weight='bold');
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In [46]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# by category of the image pixel values
##################################
scatterplot = sns.FacetGrid(imageEDA, col="Class", height=6)
scatterplot.map_dataframe(sns.scatterplot, x='Mean', y='StDev', alpha=0.5)
scatterplot.set_titles(col_template="{col_name}", row_template="{row_name}", size=18)
scatterplot.fig.subplots_adjust(top=.8)
scatterplot.fig.suptitle('Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold')
axes = scatterplot.axes.flatten()
axes[0].set_ylabel('Image Pixel Intensity Standard Deviation')
for ax in axes:
    ax.set_xlabel('Image Pixel Intensity Mean')
scatterplot.fig.tight_layout()
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In [47]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
##################################
def getImage(path):
    image = cv2.imread(path)
    resized_image = cv2.resize(image, (300,300))
    return OffsetImage(resized_image, zoom = 0.1)

DF_sample = imageEDA.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(0,120)
plt.title('Overall: Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path in zip(DF_sample['Mean'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)
    ax.add_artist(ab)
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In [48]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Glioma class
##################################
path_glioma = (os.path.join("..", DATASETS_FINAL_TRAIN_PATH,'glioma/'))
imageEDA_glioma = imageEDA.loc[imageEDA['Class'] == 'Glioma']

DF_sample = imageEDA_glioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('Glioma: Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_glioma in zip(DF_sample['Mean'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_glioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [49]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Viral Pneumonia class
##################################
path_meningioma = (os.path.join("..", DATASETS_FINAL_TRAIN_PATH,'meningioma/'))
imageEDA_meningioma = imageEDA.loc[imageEDA['Class'] == 'Meningioma']

DF_sample = imageEDA_meningioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('Meningioma: Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_meningioma in zip(DF_sample['Mean'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_meningioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [50]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Pituitary class
##################################
path_pituitary = (os.path.join("..", DATASETS_FINAL_TRAIN_PATH,'pituitary/'))
imageEDA_pituitary = imageEDA.loc[imageEDA['Class'] == 'Pituitary']

DF_sample = imageEDA_pituitary.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(0, 140)
ax.set_ylim(10,110)
plt.title('Pituitary: Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_pituitary in zip(DF_sample['Mean'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_pituitary), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [51]:
##################################
# Formulating the mean and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the No Tumor class
##################################
path_no_tumor = (os.path.join("..", DATASETS_FINAL_TRAIN_PATH,'notumor/'))
imageEDA_no_tumor = imageEDA.loc[imageEDA['Class'] == 'No Tumor']

DF_sample = imageEDA_no_tumor.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Mean", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Mean', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('No Tumor: Image Pixel Intensity Mean and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_no_tumor in zip(DF_sample['Mean'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_no_tumor), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [52]:
#################################
# Formulating the minimum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
##################################
DF_sample = imageEDA.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Min", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Minimum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(0,120)
plt.title('Overall: Image Pixel Intensity Minimum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path in zip(DF_sample['Min'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [53]:
##################################
# Formulating the minimum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Glioma class
##################################
DF_sample = imageEDA_glioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Min", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Minimum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('Glioma: Image Pixel Intensity Minimum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_glioma in zip(DF_sample['Min'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_glioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [54]:
##################################
# Formulating the minimum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Meningioma class
##################################
DF_sample = imageEDA_meningioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Min", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Minimum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('Meningioma: Image Pixel Intensity Minimum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_meningioma in zip(DF_sample['Min'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_meningioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [55]:
##################################
# Formulating the minimum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Pituitary class
##################################
DF_sample = imageEDA_pituitary.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Min", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Minimum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('Pituitary: Image Pixel Intensity Minimum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_pituitary in zip(DF_sample['Min'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_pituitary), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [56]:
##################################
# Formulating the minimum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the No Tumor class
##################################
DF_sample = imageEDA_no_tumor.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Min", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Minimum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(-5,145)
ax.set_ylim(10,110)
plt.title('No Tumor: Image Pixel Intensity Minimum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_no_tumor in zip(DF_sample['Min'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_no_tumor), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [57]:
#################################
# Formulating the maximum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
##################################
DF_sample = imageEDA.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Max", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Maximum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(115,265)
ax.set_ylim(0,120)
plt.title('Overall: Image Pixel Intensity Maximum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path in zip(DF_sample['Max'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [58]:
##################################
# Formulating the maximum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Glioma class
##################################
DF_sample = imageEDA_glioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Max", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Maximum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(115,265)
ax.set_ylim(10,110)
plt.title('Glioma: Image Pixel Intensity Maximum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_glioma in zip(DF_sample['Max'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_glioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [59]:
##################################
# Formulating the maximum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Meningioma class
##################################
DF_sample = imageEDA_meningioma.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Max", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Maximum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(115,265)
ax.set_ylim(10,110)
plt.title('Meningioma: Image Pixel Intensity Maximum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_meningioma in zip(DF_sample['Max'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_meningioma), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [60]:
##################################
# Formulating the maximum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the Pituitary class
##################################
DF_sample = imageEDA_pituitary.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Max", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Maximum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(115,265)
ax.set_ylim(10,110)
plt.title('Pituitary: Image Pixel Intensity Maximum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_pituitary in zip(DF_sample['Max'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_pituitary), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image
In [61]:
##################################
# Formulating the maximum and standard deviation 
# scatterplot distribution
# of the image pixel values
# represented as actual images
# for the No Tumor class
##################################
DF_sample = imageEDA_no_tumor.sample(frac=1.0, replace=False, random_state=123)
paths = DF_sample['Path']

fig, ax = plt.subplots(figsize=(15,9))
ab = sns.scatterplot(data=DF_sample, x="Max", y='StDev')
sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Image Pixel Intensity Maximum', fontsize=14, weight='bold')
ax.set_ylabel('Image Pixel Intensity Standard Deviation', fontsize=14, weight='bold')
ax.set_xlim(115,265)
ax.set_ylim(10,110)
plt.title('No Tumor: Image Pixel Intensity Maximum and Standard Deviation Distribution', fontsize=14, weight='bold');

for x0, y0, path_no_tumor in zip(DF_sample['Max'], DF_sample['StDev'], paths):
    ab = AnnotationBbox(getImage(path_no_tumor), (x0, y0), frameon=False)
    ax.add_artist(ab)
No description has been provided for this image

1.5.2 Hypothesis Testing ¶

1.6 Predictive Model Development ¶

1.6.1 Pre-Modelling Data Preparation ¶

1.6.2 Data Splitting ¶

1.6.3 Convolutional Neural Network Sequential Layer Development ¶

In [62]:
##################################
# Defining a function for
# plotting the loss profile
# of the training and validation sets
#################################
def plot_training_history(history, model_name):
    plt.figure(figsize=(12, 8))
    
    # Plotting training and validation loss
    plt.subplot(2, 1, 1)  # First subplot for loss
    plt.plot(history.history['loss'], label='Train Loss', color='blue')
    plt.plot(history.history['val_loss'], label='Validation Loss', color='orange')
    plt.title(f'{model_name} Training and Validation Loss', fontsize=16, weight='bold', pad=20)
    plt.ylim(-0.2, 2.2)
    plt.yticks([x * 0.50 for x in range(0, 5)])
    plt.xlim(-1, 21)
    plt.xticks([x for x in range(0, 21)])
    plt.xlabel('Epoch', fontsize=14, weight='bold')
    plt.ylabel('Loss', fontsize=14, weight='bold')
    plt.legend(loc='upper right')
    plt.grid(True)
    
    # Plotting training and validation recall
    plt.subplot(2, 1, 2)  # Second subplot for recall
    plt.plot(history.history['recall'], label='Train Recall', color='green')
    plt.plot(history.history['val_recall'], label='Validation Recall', color='red')
    plt.title(f'{model_name} Training and Validation Recall', fontsize=16, weight='bold', pad=20)
    plt.ylim(-0.1, 1.1) 
    plt.yticks([x * 0.25 for x in range(0, 5)])
    plt.xlim(-1, 21)
    plt.xticks([x for x in range(0, 21)])
    plt.xlabel('Epoch', fontsize=14, weight='bold')
    plt.ylabel('Recall', fontsize=14, weight='bold')
    plt.legend(loc='lower right')
    plt.grid(True)
    
    # Adjusting layout and show the plots
    plt.tight_layout(pad=2.0)
    plt.show()
In [63]:
##################################
# Defining the model file paths
#################################
NR_BEST_MODEL_PATH = os.path.join("..", MODELS_PATH, "nr_best_model.keras")
DR_BEST_MODEL_PATH = os.path.join("..", MODELS_PATH, "dr_best_model.keras")
BNR_BEST_MODEL_PATH = os.path.join("..", MODELS_PATH, "bnr_best_model.keras")
DR_BNR_BEST_MODEL_PATH = os.path.join("..", MODELS_PATH, "dr_bnr_best_model.keras")
In [64]:
##################################
# Defining the model callback configuration
# for model training
#################################
early_stopping = EarlyStopping(
    monitor='val_loss',               # Defining the metric to monitor
    patience=10,                      # Defining the number of epochs to wait before stopping if no improvement
    min_delta=1e-4 ,                  # Defining the minimum change in the monitored quantity to qualify as an improvement
    restore_best_weights=True         # Restoring the weights from the best epoch
)

reduce_lr = ReduceLROnPlateau(
    monitor='val_loss',               # Defining the metric to monitor
    factor=0.1,                       # Reducing the learning rate by a factor of 10%
    patience=3,                       # Defining the number of epochs to wait before reducing learning rate
    min_lr=1e-6                       # Defining the lower bound on the learning rate
)

nr_model_checkpoint = ModelCheckpoint(
    filepath=NR_BEST_MODEL_PATH,      # Defining the file path for saving
    monitor='val_loss',               # Defining the metric to monitor
    save_best_only=True,              # Saving only the best model
    save_weights_only=False,          # Saving the entire model, not just weights
)

dr_model_checkpoint = ModelCheckpoint(
    filepath=DR_BEST_MODEL_PATH,      # Defining the file path for saving
    monitor='val_loss',               # Defining the metric to monitor
    save_best_only=True,              # Saving only the best model
    save_weights_only=False,          # Saving the entire model, not just weights
)

bnr_model_checkpoint = ModelCheckpoint(
    filepath=BNR_BEST_MODEL_PATH,     # Defining the file path for saving
    monitor='val_loss',               # Defining the metric to monitor
    save_best_only=True,              # Saving only the best model
    save_weights_only=False,          # Saving the entire model, not just weights
)

dr_bnr_model_checkpoint = ModelCheckpoint(
    filepath=DR_BNR_BEST_MODEL_PATH,  # Defining the file path for saving
    monitor='val_loss',               # Defining the metric to monitor
    save_best_only=True,              # Saving only the best model
    save_weights_only=False,          # Saving the entire model, not just weights
)

1.6.3.1 CNN With No Regularization ¶

In [65]:
##################################
# Formulating the network architecture
# for CNN with no regularization
##################################
set_seed()
batch_size = 32
model_nr = Sequential()
model_nr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="nr_conv2d_0"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_0"))
model_nr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="nr_conv2d_1"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_1"))
model_nr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="nr_conv2d_2"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_2"))
model_nr.add(Flatten(name="nr_flatten"))
model_nr.add(Dense(units=128, activation='relu', name="nr_dense_0"))
model_nr.add(Dense(units=num_classes, activation='softmax', name="nr_dense_1"))
In [66]:
##################################
# Displaying the model summary
# for CNN with no regularization
##################################
print(model_nr.summary())

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ nr_conv2d_0 (Conv2D)                 │ (None, 227, 227, 16)        │             160 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_max_pooling2d_0 (MaxPooling2D)    │ (None, 113, 113, 16)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_conv2d_1 (Conv2D)                 │ (None, 113, 113, 32)        │           4,640 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_max_pooling2d_1 (MaxPooling2D)    │ (None, 56, 56, 32)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_conv2d_2 (Conv2D)                 │ (None, 56, 56, 64)          │          18,496 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_max_pooling2d_2 (MaxPooling2D)    │ (None, 28, 28, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_flatten (Flatten)                 │ (None, 50176)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_dense_0 (Dense)                   │ (None, 128)                 │       6,422,656 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ nr_dense_1 (Dense)                   │ (None, 4)                   │             516 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 6,446,468 (24.59 MB)

 Trainable params: 6,446,468 (24.59 MB)

 Non-trainable params: 0 (0.00 B)

None
In [67]:
##################################
# Displaying the model layers
# for CNN with no regularization
##################################
model_nr_layer_names = [layer.name for layer in model_nr.layers]
print("Layer Names:", model_nr_layer_names)

Layer Names: ['nr_conv2d_0', 'nr_max_pooling2d_0', 'nr_conv2d_1', 'nr_max_pooling2d_1', 'nr_conv2d_2', 'nr_max_pooling2d_2', 'nr_flatten', 'nr_dense_0', 'nr_dense_1']
In [68]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with no regularization
##################################
for layer in model_nr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
        

Layer: nr_conv2d_0, Number of Weights: 2
Layer: nr_max_pooling2d_0, Number of Weights: 0
Layer: nr_conv2d_1, Number of Weights: 2
Layer: nr_max_pooling2d_1, Number of Weights: 0
Layer: nr_conv2d_2, Number of Weights: 2
Layer: nr_max_pooling2d_2, Number of Weights: 0
Layer: nr_flatten, Number of Weights: 0
Layer: nr_dense_0, Number of Weights: 2
Layer: nr_dense_1, Number of Weights: 2
In [69]:
##################################
# Displaying the number of parameters
# for each model layer
# for CNN with no regularization
##################################
total_parameters = 0
for layer in model_nr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: nr_conv2d_0, Parameters: 160
Layer: nr_max_pooling2d_0, Parameters: 0
Layer: nr_conv2d_1, Parameters: 4640
Layer: nr_max_pooling2d_1, Parameters: 0
Layer: nr_conv2d_2, Parameters: 18496
Layer: nr_max_pooling2d_2, Parameters: 0
Layer: nr_flatten, Parameters: 0
Layer: nr_dense_0, Parameters: 6422656
Layer: nr_dense_1, Parameters: 516

Total Parameters in the Model: 6446468

1.6.3.2 CNN With Dropout Regularization ¶

In [70]:
##################################
# Formulating the network architecture
# for CNN with dropout regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_dr = Sequential()
model_dr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="dr_conv2d_0"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_0"))
model_dr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_conv2d_1"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_1"))
model_dr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_conv2d_2"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_2"))
model_dr.add(Flatten(name="dr_flatten"))
model_dr.add(Dense(units=128, activation='relu', name="dr_dense_0"))
model_dr.add(Dropout(rate=0.30, name="dr_dropout"))
model_dr.add(Dense(units=num_classes, activation='softmax', name="dr_dense_1"))
In [71]:
##################################
# Displaying the model summary
# for CNN with dropout regularization
##################################
print(model_dr.summary())

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dr_conv2d_0 (Conv2D)                 │ (None, 227, 227, 16)        │             160 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_max_pooling2d_0 (MaxPooling2D)    │ (None, 113, 113, 16)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_conv2d_1 (Conv2D)                 │ (None, 113, 113, 32)        │           4,640 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_max_pooling2d_1 (MaxPooling2D)    │ (None, 56, 56, 32)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_conv2d_2 (Conv2D)                 │ (None, 56, 56, 64)          │          18,496 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_max_pooling2d_2 (MaxPooling2D)    │ (None, 28, 28, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_flatten (Flatten)                 │ (None, 50176)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_dense_0 (Dense)                   │ (None, 128)                 │       6,422,656 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_dropout (Dropout)                 │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_dense_1 (Dense)                   │ (None, 4)                   │             516 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 6,446,468 (24.59 MB)

 Trainable params: 6,446,468 (24.59 MB)

 Non-trainable params: 0 (0.00 B)

None
In [72]:
##################################
# Displaying the model layers
# for CNN with dropout regularization
##################################
model_dr_layer_names = [layer.name for layer in model_dr.layers]
print("Layer Names:", model_dr_layer_names)

Layer Names: ['dr_conv2d_0', 'dr_max_pooling2d_0', 'dr_conv2d_1', 'dr_max_pooling2d_1', 'dr_conv2d_2', 'dr_max_pooling2d_2', 'dr_flatten', 'dr_dense_0', 'dr_dropout', 'dr_dense_1']
In [73]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout regularization
##################################
for layer in model_dr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")
        

Layer: dr_conv2d_0, Number of Weights: 2
Layer: dr_max_pooling2d_0, Number of Weights: 0
Layer: dr_conv2d_1, Number of Weights: 2
Layer: dr_max_pooling2d_1, Number of Weights: 0
Layer: dr_conv2d_2, Number of Weights: 2
Layer: dr_max_pooling2d_2, Number of Weights: 0
Layer: dr_flatten, Number of Weights: 0
Layer: dr_dense_0, Number of Weights: 2
Layer: dr_dropout, Number of Weights: 0
Layer: dr_dense_1, Number of Weights: 2
In [74]:
##################################
# Displaying the number of parameters
# for each model layer
# for CNN with dropout regularization
##################################
total_parameters = 0
for layer in model_dr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: dr_conv2d_0, Parameters: 160
Layer: dr_max_pooling2d_0, Parameters: 0
Layer: dr_conv2d_1, Parameters: 4640
Layer: dr_max_pooling2d_1, Parameters: 0
Layer: dr_conv2d_2, Parameters: 18496
Layer: dr_max_pooling2d_2, Parameters: 0
Layer: dr_flatten, Parameters: 0
Layer: dr_dense_0, Parameters: 6422656
Layer: dr_dropout, Parameters: 0
Layer: dr_dense_1, Parameters: 516

Total Parameters in the Model: 6446468

1.6.3.3 CNN With Batch Normalization Regularization ¶

In [75]:
##################################
# Formulating the network architecture
# for CNN with batch normalization regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_bnr = Sequential()
model_bnr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="bnr_conv2d_0"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_0"))
model_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="bnr_conv2d_1"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_1"))
model_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="bnr_conv2d_2"))
model_bnr.add(BatchNormalization(name="bnr_batch_normalization"))
model_bnr.add(Activation('relu', name="bnr_activation"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_2"))
model_bnr.add(Flatten(name="bnr_flatten"))
model_bnr.add(Dense(units=128, activation='relu', name="bnr_dense_0"))
model_bnr.add(Dense(units=num_classes, activation='softmax', name="bnr_dense_1"))
In [76]:
##################################
# Displaying the model summary
# for CNN with batch normalization regularization
##################################
print(model_bnr.summary())

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ bnr_conv2d_0 (Conv2D)                │ (None, 227, 227, 16)        │             160 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_max_pooling2d_0 (MaxPooling2D)   │ (None, 113, 113, 16)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_conv2d_1 (Conv2D)                │ (None, 113, 113, 32)        │           4,640 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_max_pooling2d_1 (MaxPooling2D)   │ (None, 56, 56, 32)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_conv2d_2 (Conv2D)                │ (None, 56, 56, 64)          │          18,496 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_batch_normalization              │ (None, 56, 56, 64)          │             256 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_activation (Activation)          │ (None, 56, 56, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_max_pooling2d_2 (MaxPooling2D)   │ (None, 28, 28, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_flatten (Flatten)                │ (None, 50176)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_dense_0 (Dense)                  │ (None, 128)                 │       6,422,656 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bnr_dense_1 (Dense)                  │ (None, 4)                   │             516 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 6,446,724 (24.59 MB)

 Trainable params: 6,446,596 (24.59 MB)

 Non-trainable params: 128 (512.00 B)

None
In [77]:
##################################
# Displaying the model layers
# for CNN with batch normalization regularization
##################################
model_bnr_layer_names = [layer.name for layer in model_bnr.layers]
print("Layer Names:", model_bnr_layer_names)

Layer Names: ['bnr_conv2d_0', 'bnr_max_pooling2d_0', 'bnr_conv2d_1', 'bnr_max_pooling2d_1', 'bnr_conv2d_2', 'bnr_batch_normalization', 'bnr_activation', 'bnr_max_pooling2d_2', 'bnr_flatten', 'bnr_dense_0', 'bnr_dense_1']
In [78]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
for layer in model_bnr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: bnr_conv2d_0, Number of Weights: 2
Layer: bnr_max_pooling2d_0, Number of Weights: 0
Layer: bnr_conv2d_1, Number of Weights: 2
Layer: bnr_max_pooling2d_1, Number of Weights: 0
Layer: bnr_conv2d_2, Number of Weights: 2
Layer: bnr_batch_normalization, Number of Weights: 4
Layer: bnr_activation, Number of Weights: 0
Layer: bnr_max_pooling2d_2, Number of Weights: 0
Layer: bnr_flatten, Number of Weights: 0
Layer: bnr_dense_0, Number of Weights: 2
Layer: bnr_dense_1, Number of Weights: 2
In [79]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with batch normalization regularization
##################################
total_parameters = 0
for layer in model_bnr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: bnr_conv2d_0, Parameters: 160
Layer: bnr_max_pooling2d_0, Parameters: 0
Layer: bnr_conv2d_1, Parameters: 4640
Layer: bnr_max_pooling2d_1, Parameters: 0
Layer: bnr_conv2d_2, Parameters: 18496
Layer: bnr_batch_normalization, Parameters: 256
Layer: bnr_activation, Parameters: 0
Layer: bnr_max_pooling2d_2, Parameters: 0
Layer: bnr_flatten, Parameters: 0
Layer: bnr_dense_0, Parameters: 6422656
Layer: bnr_dense_1, Parameters: 516

Total Parameters in the Model: 6446724

1.6.3.4 CNN With Dropout and Batch Normalization Regularization ¶

In [80]:
##################################
# Formulating the network architecture
# for CNN with dropout and batch normalization regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_dr_bnr = Sequential()
model_dr_bnr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="dr_bnr_conv2d_0"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_0"))
model_dr_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_bnr_conv2d_1"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_1"))
model_dr_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_bnr_conv2d_2"))
model_dr_bnr.add(BatchNormalization(name="dr_bnr_batch_normalization"))
model_dr_bnr.add(Activation('relu', name="dr_bnr_activation"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_2"))
model_dr_bnr.add(Flatten(name="dr_bnr_flatten"))
model_dr_bnr.add(Dense(units=128, activation='relu', name="dr_bnr_dense_0"))
model_dr_bnr.add(Dropout(rate=0.30, name="dr_bnr_dropout"))
model_dr_bnr.add(Dense(units=num_classes, activation='softmax', name="dr_bnr_dense_1"))
In [81]:
##################################
# Displaying the model summary
# for CNN with dropout and batch normalization regularization
##################################
print(model_dr_bnr.summary())

Model: "sequential_3"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dr_bnr_conv2d_0 (Conv2D)             │ (None, 227, 227, 16)        │             160 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_max_pooling2d_0               │ (None, 113, 113, 16)        │               0 │
│ (MaxPooling2D)                       │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_conv2d_1 (Conv2D)             │ (None, 113, 113, 32)        │           4,640 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_max_pooling2d_1               │ (None, 56, 56, 32)          │               0 │
│ (MaxPooling2D)                       │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_conv2d_2 (Conv2D)             │ (None, 56, 56, 64)          │          18,496 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_batch_normalization           │ (None, 56, 56, 64)          │             256 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_activation (Activation)       │ (None, 56, 56, 64)          │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_max_pooling2d_2               │ (None, 28, 28, 64)          │               0 │
│ (MaxPooling2D)                       │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_flatten (Flatten)             │ (None, 50176)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_dense_0 (Dense)               │ (None, 128)                 │       6,422,656 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_dropout (Dropout)             │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dr_bnr_dense_1 (Dense)               │ (None, 4)                   │             516 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 6,446,724 (24.59 MB)

 Trainable params: 6,446,596 (24.59 MB)

 Non-trainable params: 128 (512.00 B)

None
In [82]:
##################################
# Displaying the model layers
# for CNN with dropout and batch normalization regularization
##################################
model_dr_bnr_layer_names = [layer.name for layer in model_dr_bnr.layers]
print("Layer Names:", model_dr_bnr_layer_names)

Layer Names: ['dr_bnr_conv2d_0', 'dr_bnr_max_pooling2d_0', 'dr_bnr_conv2d_1', 'dr_bnr_max_pooling2d_1', 'dr_bnr_conv2d_2', 'dr_bnr_batch_normalization', 'dr_bnr_activation', 'dr_bnr_max_pooling2d_2', 'dr_bnr_flatten', 'dr_bnr_dense_0', 'dr_bnr_dropout', 'dr_bnr_dense_1']
In [83]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout and batch normalization regularization
##################################
for layer in model_dr_bnr.layers:
    if hasattr(layer, 'weights'):
        print(f"Layer: {layer.name}, Number of Weights: {len(layer.get_weights())}")

Layer: dr_bnr_conv2d_0, Number of Weights: 2
Layer: dr_bnr_max_pooling2d_0, Number of Weights: 0
Layer: dr_bnr_conv2d_1, Number of Weights: 2
Layer: dr_bnr_max_pooling2d_1, Number of Weights: 0
Layer: dr_bnr_conv2d_2, Number of Weights: 2
Layer: dr_bnr_batch_normalization, Number of Weights: 4
Layer: dr_bnr_activation, Number of Weights: 0
Layer: dr_bnr_max_pooling2d_2, Number of Weights: 0
Layer: dr_bnr_flatten, Number of Weights: 0
Layer: dr_bnr_dense_0, Number of Weights: 2
Layer: dr_bnr_dropout, Number of Weights: 0
Layer: dr_bnr_dense_1, Number of Weights: 2
In [84]:
##################################
# Displaying the number of weights
# for each model layer
# for CNN with dropout and batch normalization regularization
##################################
total_parameters = 0
for layer in model_dr_bnr.layers:
    layer_parameters = layer.count_params()
    total_parameters += layer_parameters
    print(f"Layer: {layer.name}, Parameters: {layer_parameters}")
print("\nTotal Parameters in the Model:", total_parameters)

Layer: dr_bnr_conv2d_0, Parameters: 160
Layer: dr_bnr_max_pooling2d_0, Parameters: 0
Layer: dr_bnr_conv2d_1, Parameters: 4640
Layer: dr_bnr_max_pooling2d_1, Parameters: 0
Layer: dr_bnr_conv2d_2, Parameters: 18496
Layer: dr_bnr_batch_normalization, Parameters: 256
Layer: dr_bnr_activation, Parameters: 0
Layer: dr_bnr_max_pooling2d_2, Parameters: 0
Layer: dr_bnr_flatten, Parameters: 0
Layer: dr_bnr_dense_0, Parameters: 6422656
Layer: dr_bnr_dropout, Parameters: 0
Layer: dr_bnr_dense_1, Parameters: 516

Total Parameters in the Model: 6446724

1.6.4 CNN With No Regularization Model Fitting | Hyperparameter Tuning | Validation ¶

In [85]:
##################################
# Formulating the network architecture
# for CNN with no regularization
##################################
set_seed()
batch_size = 32
model_nr = Sequential()
model_nr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="nr_conv2d_0"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_0"))
model_nr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="nr_conv2d_1"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_1"))
model_nr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="nr_conv2d_2"))
model_nr.add(MaxPooling2D(pool_size=(2, 2), name="nr_max_pooling2d_2"))
model_nr.add(Flatten(name="nr_flatten"))
model_nr.add(Dense(units=128, activation='relu', name="nr_dense_0"))
model_nr.add(Dense(units=num_classes, activation='softmax', name="nr_dense_1"))

##################################
# Compiling the network layers
##################################
optimizer = Adam(learning_rate=0.001)
model_nr.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=[Recall(name='recall')])
In [86]:
##################################
# Fitting the model
# for CNN with no regularization
##################################
epochs = 20
set_seed()
model_nr_history = model_nr.fit(train_gen, 
                                steps_per_epoch=len(train_gen)+1,
                                validation_steps=len(val_gen)+1,
                                validation_data=val_gen, 
                                epochs=epochs,
                                verbose=1,
                                callbacks=[early_stopping, reduce_lr, nr_model_checkpoint])

Epoch 1/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 368ms/step - loss: 0.9387 - recall: 0.4232 - val_loss: 0.7573 - val_recall: 0.6950 - learning_rate: 0.0010
Epoch 2/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 352ms/step - loss: 0.3854 - recall: 0.8401 - val_loss: 0.8148 - val_recall: 0.6985 - learning_rate: 0.0010
Epoch 3/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.2359 - recall: 0.9100 - val_loss: 0.9404 - val_recall: 0.7239 - learning_rate: 0.0010
Epoch 4/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 364ms/step - loss: 0.1882 - recall: 0.9315 - val_loss: 0.7925 - val_recall: 0.7967 - learning_rate: 0.0010
Epoch 5/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 352ms/step - loss: 0.1249 - recall: 0.9541 - val_loss: 0.7649 - val_recall: 0.7932 - learning_rate: 1.0000e-04
Epoch 6/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 351ms/step - loss: 0.1104 - recall: 0.9568 - val_loss: 0.7411 - val_recall: 0.7949 - learning_rate: 1.0000e-04
Epoch 7/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 84s 367ms/step - loss: 0.0860 - recall: 0.9661 - val_loss: 0.7796 - val_recall: 0.8037 - learning_rate: 1.0000e-04
Epoch 8/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.0855 - recall: 0.9640 - val_loss: 0.8181 - val_recall: 0.8072 - learning_rate: 1.0000e-04
Epoch 9/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 354ms/step - loss: 0.0695 - recall: 0.9774 - val_loss: 0.8332 - val_recall: 0.7967 - learning_rate: 1.0000e-04
Epoch 10/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 355ms/step - loss: 0.0699 - recall: 0.9809 - val_loss: 0.8315 - val_recall: 0.8072 - learning_rate: 1.0000e-05
Epoch 11/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 351ms/step - loss: 0.0638 - recall: 0.9795 - val_loss: 0.8407 - val_recall: 0.8107 - learning_rate: 1.0000e-05
Epoch 12/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 362ms/step - loss: 0.0727 - recall: 0.9754 - val_loss: 0.8312 - val_recall: 0.8072 - learning_rate: 1.0000e-05
Epoch 13/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 357ms/step - loss: 0.0591 - recall: 0.9791 - val_loss: 0.8332 - val_recall: 0.8081 - learning_rate: 1.0000e-06
Epoch 14/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 360ms/step - loss: 0.0657 - recall: 0.9785 - val_loss: 0.8350 - val_recall: 0.8072 - learning_rate: 1.0000e-06
Epoch 15/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.0672 - recall: 0.9770 - val_loss: 0.8377 - val_recall: 0.8089 - learning_rate: 1.0000e-06
Epoch 16/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 362ms/step - loss: 0.0687 - recall: 0.9767 - val_loss: 0.8386 - val_recall: 0.8089 - learning_rate: 1.0000e-06
In [87]:
##################################
# Evaluating the model
# for CNN with no regularization
# on the independent validation set
##################################
model_nr_y_pred = model_nr.predict(val_gen)

36/36 ━━━━━━━━━━━━━━━━━━━━ 5s 139ms/step
In [88]:
##################################
# Plotting the loss profile
# for CNN with no regularization
# on the training and validation sets
##################################
plot_training_history(model_nr_history, 'CNN With No Regularization : ')
No description has been provided for this image
In [89]:
##################################
# Consolidating the predictions
# for CNN with no regularization
# on the validation set
##################################
model_nr_predictions = np.array(list(map(lambda x: np.argmax(x), model_nr_y_pred)))
model_nr_y_true = val_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_nr_y_true, model_nr_predictions), columns=classes, index =classes)

##################################
# Plotting the confusion matrix
# for CNN with no regularization
# on the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250,cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With No Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold',pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()

WARNING:tensorflow:From D:\Github_Codes\ProjectPortfolio\Portfolio_Project_56\mdeploy_venv\Lib\site-packages\keras\src\backend\common\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.
No description has been provided for this image
In [90]:
##################################
# Calculating the model accuracy
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_acc = accuracy_score(model_nr_y_true, model_nr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for the entire validation set
##################################
model_nr_results_all = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with no regularization
# for each category of the validation set
##################################
model_nr_results_class = precision_recall_fscore_support(model_nr_y_true, model_nr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with no regularization
##################################
metric_columns = ['Precision','Recall','F-Score','Support']
model_nr_all_df = pd.concat([pd.DataFrame(list(model_nr_results_class)).T,pd.DataFrame(list(model_nr_results_all)).T])
model_nr_all_df.columns = metric_columns
model_nr_all_df.index = ['No Tumor', 'Glioma', 'Meningioma', 'Pituitary', 'Total']
model_nr_all_df
Out[90]:
Precision Recall F-Score Support
No Tumor 0.873377 0.843260 0.858054 319.0
Glioma 0.920833 0.837121 0.876984 264.0
Meningioma 0.616667 0.692884 0.652557 267.0
Pituitary 0.819113 0.824742 0.821918 291.0
Total 0.807497 0.799502 0.802378 NaN

1.6.5 CNN With Dropout Regularization Model Fitting | Hyperparameter Tuning | Validation ¶

In [91]:
##################################
# Formulating the network architecture
# for CNN with dropout regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_dr = Sequential()
model_dr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="dr_conv2d_0"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_0"))
model_dr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_conv2d_1"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_1"))
model_dr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_conv2d_2"))
model_dr.add(MaxPooling2D(pool_size=(2, 2), name="dr_max_pooling2d_2"))
model_dr.add(Flatten(name="dr_flatten"))
model_dr.add(Dense(units=128, activation='relu', name="dr_dense_0"))
model_dr.add(Dropout(rate=0.30, name="dr_dropout"))
model_dr.add(Dense(units=num_classes, activation='softmax', name="dr_dense_1"))

##################################
# Compiling the network layers
##################################
optimizer = Adam(learning_rate=0.001)
model_dr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall(name='recall')])
In [92]:
##################################
# Fitting the model
# for CNN with dropout regularization
##################################
epochs = 20
set_seed()
model_dr_history = model_dr.fit(train_gen, 
                                steps_per_epoch=len(train_gen)+1,
                                validation_steps=len(val_gen)+1,
                                validation_data=val_gen, 
                                epochs=epochs,
                                verbose=1,
                                callbacks=[early_stopping, reduce_lr, dr_model_checkpoint])

Epoch 1/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 56s 377ms/step - loss: 1.0770 - recall: 0.3361 - val_loss: 0.8354 - val_recall: 0.6757 - learning_rate: 0.0010
Epoch 2/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 363ms/step - loss: 0.4318 - recall: 0.8098 - val_loss: 0.7435 - val_recall: 0.7073 - learning_rate: 0.0010
Epoch 3/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 362ms/step - loss: 0.3128 - recall: 0.8685 - val_loss: 0.7023 - val_recall: 0.7704 - learning_rate: 0.0010
Epoch 4/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 363ms/step - loss: 0.2648 - recall: 0.8899 - val_loss: 0.7189 - val_recall: 0.7485 - learning_rate: 0.0010
Epoch 5/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.2105 - recall: 0.9192 - val_loss: 0.7316 - val_recall: 0.7791 - learning_rate: 0.0010
Epoch 6/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.1848 - recall: 0.9329 - val_loss: 0.6052 - val_recall: 0.7958 - learning_rate: 0.0010
Epoch 7/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 359ms/step - loss: 0.1397 - recall: 0.9493 - val_loss: 0.8291 - val_recall: 0.7879 - learning_rate: 0.0010
Epoch 8/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 364ms/step - loss: 0.1444 - recall: 0.9431 - val_loss: 0.7666 - val_recall: 0.8063 - learning_rate: 0.0010
Epoch 9/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 362ms/step - loss: 0.1201 - recall: 0.9517 - val_loss: 0.9356 - val_recall: 0.7415 - learning_rate: 0.0010
Epoch 10/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 363ms/step - loss: 0.0917 - recall: 0.9687 - val_loss: 0.7669 - val_recall: 0.8221 - learning_rate: 1.0000e-04
Epoch 11/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 54s 377ms/step - loss: 0.0670 - recall: 0.9774 - val_loss: 0.8218 - val_recall: 0.8151 - learning_rate: 1.0000e-04
Epoch 12/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 53s 368ms/step - loss: 0.0740 - recall: 0.9748 - val_loss: 0.8106 - val_recall: 0.8195 - learning_rate: 1.0000e-04
Epoch 13/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 53s 371ms/step - loss: 0.0559 - recall: 0.9776 - val_loss: 0.8149 - val_recall: 0.8203 - learning_rate: 1.0000e-05
Epoch 14/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 382ms/step - loss: 0.0587 - recall: 0.9824 - val_loss: 0.8228 - val_recall: 0.8203 - learning_rate: 1.0000e-05
Epoch 15/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 51s 357ms/step - loss: 0.0637 - recall: 0.9741 - val_loss: 0.8297 - val_recall: 0.8203 - learning_rate: 1.0000e-05
Epoch 16/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 52s 362ms/step - loss: 0.0574 - recall: 0.9784 - val_loss: 0.8297 - val_recall: 0.8212 - learning_rate: 1.0000e-06
In [93]:
##################################
# Evaluating the model
# for CNN with dropout regularization
# on the independent validation set
##################################
model_dr_y_pred = model_dr.predict(val_gen)

36/36 ━━━━━━━━━━━━━━━━━━━━ 5s 140ms/step
In [94]:
##################################
# Plotting the loss profile
# for CNN with dropout regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_history, 'CNN With Dropout Regularization : ')
No description has been provided for this image
In [95]:
##################################
# Consolidating the predictions
# for CNN with dropout regularization
# on the validation set
##################################
model_dr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_y_pred)))
model_dr_y_true=val_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with dropout regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_y_true, model_dr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250, cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold', pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
No description has been provided for this image
In [96]:
##################################
# Calculating the model accuracy
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_acc = accuracy_score(model_dr_y_true, model_dr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for the entire validation set
##################################
model_dr_results_all = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average='macro',zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout regularization
# for each category of the validation set
##################################
model_dr_results_class = precision_recall_fscore_support(model_dr_y_true, model_dr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_all_df = pd.concat([pd.DataFrame(list(model_dr_results_class)).T,pd.DataFrame(list(model_dr_results_all)).T])
model_dr_all_df.columns = metric_columns
model_dr_all_df.index = ['No Tumor', 'Glioma', 'Meningioma', 'Pituitary', 'Total']
model_dr_all_df
Out[96]:
Precision Recall F-Score Support
No Tumor 0.819484 0.896552 0.856287 319.0
Glioma 0.896825 0.856061 0.875969 264.0
Meningioma 0.638783 0.629213 0.633962 267.0
Pituitary 0.848375 0.807560 0.827465 291.0
Total 0.800867 0.797346 0.798421 NaN

1.6.6 CNN With Batch Normalization Regularization Model Fitting | Hyperparameter Tuning | Validation ¶

In [97]:
##################################
# Formulating the network architecture
# for CNN with batch normalization regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_bnr = Sequential()
model_bnr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="bnr_conv2d_0"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_0"))
model_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="bnr_conv2d_1"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_1"))
model_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="bnr_conv2d_2"))
model_bnr.add(BatchNormalization(name="bnr_batch_normalization"))
model_bnr.add(Activation('relu', name="bnr_activation"))
model_bnr.add(MaxPooling2D(pool_size=(2, 2), name="bnr_max_pooling2d_2"))
model_bnr.add(Flatten(name="bnr_flatten"))
model_bnr.add(Dense(units=128, activation='relu', name="bnr_dense_0"))
model_bnr.add(Dense(units=num_classes, activation='softmax', name="bnr_dense_1"))

##################################
# Compiling the network layers
##################################
optimizer = Adam(learning_rate=0.001)
model_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall(name='recall')])
In [98]:
##################################
# Fitting the model
# for CNN with batch normalization regularization
##################################
epochs = 20
set_seed()
model_bnr_history = model_bnr.fit(train_gen, 
                                  steps_per_epoch=len(train_gen)+1,
                                  validation_steps=len(val_gen)+1,
                                  validation_data=val_gen, 
                                  epochs=epochs,
                                  verbose=1,
                                  callbacks=[early_stopping, reduce_lr, bnr_model_checkpoint])

Epoch 1/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 61s 412ms/step - loss: 2.0890 - recall: 0.5036 - val_loss: 1.1336 - val_recall: 0.0167 - learning_rate: 0.0010
Epoch 2/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 79s 388ms/step - loss: 0.3362 - recall: 0.8611 - val_loss: 0.9812 - val_recall: 0.1087 - learning_rate: 0.0010
Epoch 3/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 82s 390ms/step - loss: 0.2292 - recall: 0.9119 - val_loss: 0.6711 - val_recall: 0.6740 - learning_rate: 0.0010
Epoch 4/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.1786 - recall: 0.9340 - val_loss: 1.0136 - val_recall: 0.5285 - learning_rate: 0.0010
Epoch 5/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 57s 395ms/step - loss: 0.1684 - recall: 0.9306 - val_loss: 1.1061 - val_recall: 0.6854 - learning_rate: 0.0010
Epoch 6/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.1131 - recall: 0.9553 - val_loss: 0.9477 - val_recall: 0.7625 - learning_rate: 0.0010
Epoch 7/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.0678 - recall: 0.9768 - val_loss: 0.7528 - val_recall: 0.8273 - learning_rate: 1.0000e-04
Epoch 8/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.0606 - recall: 0.9798 - val_loss: 0.7445 - val_recall: 0.8256 - learning_rate: 1.0000e-04
Epoch 9/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 385ms/step - loss: 0.0451 - recall: 0.9870 - val_loss: 0.7859 - val_recall: 0.8335 - learning_rate: 1.0000e-04
Epoch 10/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 380ms/step - loss: 0.0497 - recall: 0.9858 - val_loss: 0.7841 - val_recall: 0.8352 - learning_rate: 1.0000e-05
In [99]:
##################################
# Evaluating the model
# for CNN with batch normalization regularization
# on the independent validation set
##################################
model_bnr_y_pred = model_bnr.predict(val_gen)

36/36 ━━━━━━━━━━━━━━━━━━━━ 5s 144ms/step
In [100]:
##################################
# Plotting the loss profile
# for CNN with batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_bnr_history, 'CNN With Batch Normalization Regularization : ')
No description has been provided for this image
In [101]:
##################################
# Consolidating the predictions
# for CNN with batch normalization regularization
# on the validation set
##################################
model_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_bnr_y_pred)))
model_bnr_y_true=val_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_bnr_y_true, model_bnr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250, cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold', pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
No description has been provided for this image
In [102]:
##################################
# Calculating the model accuracy
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_acc = accuracy_score(model_bnr_y_true, model_bnr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for the entire validation set
##################################
model_bnr_results_all = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average='macro', zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with batch normalization regularization
# for each category of the validation set
##################################
model_bnr_results_class = precision_recall_fscore_support(model_bnr_y_true, model_bnr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_bnr_all_df = pd.concat([pd.DataFrame(list(model_bnr_results_class)).T,pd.DataFrame(list(model_bnr_results_all)).T])
model_bnr_all_df.columns = metric_columns
model_bnr_all_df.index = ['No Tumor', 'Glioma', 'Meningioma', 'Pituitary', 'Total']
model_bnr_all_df
Out[102]:
Precision Recall F-Score Support
No Tumor 0.839080 0.686520 0.755172 319.0
Glioma 0.702624 0.912879 0.794069 264.0
Meningioma 0.423963 0.344569 0.380165 267.0
Pituitary 0.703125 0.773196 0.736498 291.0
Total 0.667198 0.679291 0.666476 NaN

1.6.7 CNN With Dropout and Batch Normalization Regularization Model Fitting | Hyperparameter Tuning | Validation ¶

In [103]:
##################################
# Formulating the network architecture
# for CNN with dropout and batch normalization regularization
##################################
set_seed()
batch_size = 32
input_shape = (227, 227, 1)
model_dr_bnr = Sequential()
model_dr_bnr.add(Conv2D(filters=16, kernel_size=(3, 3), padding = 'Same', activation='relu', input_shape=(227, 227, 1), name="dr_bnr_conv2d_0"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_0"))
model_dr_bnr.add(Conv2D(filters=32, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_bnr_conv2d_1"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_1"))
model_dr_bnr.add(Conv2D(filters=64, kernel_size=(3, 3), padding = 'Same', activation='relu', name="dr_bnr_conv2d_2"))
model_dr_bnr.add(BatchNormalization(name="dr_bnr_batch_normalization"))
model_dr_bnr.add(Activation('relu', name="dr_bnr_activation"))
model_dr_bnr.add(MaxPooling2D(pool_size=(2, 2), name="dr_bnr_max_pooling2d_2"))
model_dr_bnr.add(Flatten(name="dr_bnr_flatten"))
model_dr_bnr.add(Dense(units=128, activation='relu', name="dr_bnr_dense_0"))
model_dr_bnr.add(Dropout(rate=0.30, name="dr_bnr_dropout"))
model_dr_bnr.add(Dense(units=num_classes, activation='softmax', name="dr_bnr_dense_1"))

##################################
# Compiling the network layers
##################################
optimizer = Adam(learning_rate=0.001)
model_dr_bnr.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[Recall(name='recall')])
In [104]:
##################################
# Fitting the model
# for CNN with dropout and batch normalization regularization
##################################
epochs = 20
set_seed()
model_dr_bnr_history = model_dr_bnr.fit(train_gen, 
                                  steps_per_epoch=len(train_gen)+1,
                                  validation_steps=len(val_gen)+1,
                                  validation_data=val_gen, 
                                  epochs=epochs,
                                  verbose=1,
                                  callbacks=[early_stopping, reduce_lr, dr_bnr_model_checkpoint])

Epoch 1/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 58s 388ms/step - loss: 1.7995 - recall: 0.5219 - val_loss: 1.1321 - val_recall: 0.0342 - learning_rate: 0.0010
Epoch 2/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.3938 - recall: 0.8333 - val_loss: 0.9887 - val_recall: 0.0649 - learning_rate: 0.0010
Epoch 3/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 384ms/step - loss: 0.2484 - recall: 0.8988 - val_loss: 0.6290 - val_recall: 0.6713 - learning_rate: 0.0010
Epoch 4/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 380ms/step - loss: 0.2268 - recall: 0.9093 - val_loss: 0.6252 - val_recall: 0.7555 - learning_rate: 0.0010
Epoch 5/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 380ms/step - loss: 0.1590 - recall: 0.9359 - val_loss: 0.8430 - val_recall: 0.7046 - learning_rate: 0.0010
Epoch 6/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 62s 434ms/step - loss: 0.1436 - recall: 0.9409 - val_loss: 0.5680 - val_recall: 0.8352 - learning_rate: 0.0010
Epoch 7/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 56s 387ms/step - loss: 0.1110 - recall: 0.9563 - val_loss: 0.7335 - val_recall: 0.8344 - learning_rate: 0.0010
Epoch 8/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.1024 - recall: 0.9607 - val_loss: 0.9613 - val_recall: 0.8291 - learning_rate: 0.0010
Epoch 9/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 381ms/step - loss: 0.1047 - recall: 0.9615 - val_loss: 0.6784 - val_recall: 0.8475 - learning_rate: 0.0010
Epoch 10/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.0612 - recall: 0.9779 - val_loss: 0.7055 - val_recall: 0.8580 - learning_rate: 1.0000e-04
Epoch 11/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 382ms/step - loss: 0.0465 - recall: 0.9825 - val_loss: 0.7504 - val_recall: 0.8615 - learning_rate: 1.0000e-04
Epoch 12/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 380ms/step - loss: 0.0426 - recall: 0.9825 - val_loss: 0.8035 - val_recall: 0.8624 - learning_rate: 1.0000e-04
Epoch 13/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 383ms/step - loss: 0.0373 - recall: 0.9885 - val_loss: 0.7971 - val_recall: 0.8624 - learning_rate: 1.0000e-05
Epoch 14/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 384ms/step - loss: 0.0427 - recall: 0.9842 - val_loss: 0.7896 - val_recall: 0.8606 - learning_rate: 1.0000e-05
Epoch 15/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 55s 385ms/step - loss: 0.0323 - recall: 0.9903 - val_loss: 0.7911 - val_recall: 0.8606 - learning_rate: 1.0000e-05
Epoch 16/20
144/144 ━━━━━━━━━━━━━━━━━━━━ 56s 386ms/step - loss: 0.0418 - recall: 0.9818 - val_loss: 0.7901 - val_recall: 0.8606 - learning_rate: 1.0000e-06
In [105]:
##################################
# Evaluating the model
# for CNN with dropout and batch normalization regularization
# on the independent validation set
##################################
model_dr_bnr_y_pred = model_dr_bnr.predict(val_gen)

36/36 ━━━━━━━━━━━━━━━━━━━━ 5s 138ms/step
In [106]:
##################################
# Plotting the loss profile
# for CNN with dropout and batch normalization regularization
# on the training and validation sets
##################################
plot_training_history(model_dr_bnr_history, 'CNN With Dropout and Batch Normalization regularization : ')
No description has been provided for this image
In [107]:
##################################
# Consolidating the predictions
# for CNN with dropout and batch normalization regularization
# on the validation set
##################################
model_dr_bnr_predictions = np.array(list(map(lambda x: np.argmax(x), model_dr_bnr_y_pred)))
model_dr_bnr_y_true=val_gen.classes

##################################
# Formulating the confusion matrix
# for CNN with dropout and batch normalization regularization
# on the validation set
##################################
CMatrix = pd.DataFrame(confusion_matrix(model_dr_bnr_y_true, model_dr_bnr_predictions), columns=classes, index =classes)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and batch normalization regularization
# for each category of the validation set
##################################
plt.figure(figsize=(10, 6))
ax = sns.heatmap(CMatrix, annot = True, fmt = 'g' ,vmin = 0, vmax = 250, cmap = 'icefire')
ax.set_xlabel('Predicted',fontsize = 14,weight = 'bold')
ax.set_xticklabels(ax.get_xticklabels(),rotation =0)
ax.set_ylabel('Actual',fontsize = 14,weight = 'bold') 
ax.set_yticklabels(ax.get_yticklabels(),rotation =0)
ax.set_title('CNN With Dropout and Batch Normalization Regularization : Validation Set Confusion Matrix',fontsize = 14, weight = 'bold', pad=20);

##################################
# Resetting all states generated by Keras
##################################
keras.backend.clear_session()
No description has been provided for this image
In [108]:
##################################
# Calculating the model accuracy
# for CNN with dropout and batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_acc = accuracy_score(model_dr_bnr_y_true, model_dr_bnr_predictions)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and batch normalization regularization
# for the entire validation set
##################################
model_dr_bnr_results_all = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average='macro', zero_division = 1)

##################################
# Calculating the model 
# Precision, Recall, F-score and Support
# for CNN with dropout and batch normalization regularization
# for each category of the validation set
##################################
model_dr_bnr_results_class = precision_recall_fscore_support(model_dr_bnr_y_true, model_dr_bnr_predictions, average=None, zero_division = 1)

##################################
# Consolidating all model evaluation metrics 
# for CNN with dropout and batch normalization regularization
##################################
metric_columns = ['Precision','Recall', 'F-Score','Support']
model_dr_bnr_all_df = pd.concat([pd.DataFrame(list(model_dr_bnr_results_class)).T,pd.DataFrame(list(model_dr_bnr_results_all)).T])
model_dr_bnr_all_df.columns = metric_columns
model_dr_bnr_all_df.index = ['No Tumor', 'Glioma', 'Meningioma', 'Pituitary', 'Total']
model_dr_bnr_all_df
Out[108]:
Precision Recall F-Score Support
No Tumor 0.848765 0.862069 0.855365 319.0
Glioma 0.923077 0.818182 0.867470 264.0
Meningioma 0.753968 0.711610 0.732177 267.0
Pituitary 0.845921 0.962199 0.900322 291.0
Total 0.842933 0.838515 0.838834 NaN

1.6.8 Model Selection ¶

1.6.9 Model Testing ¶

1.6.10 Model Inference ¶

1.7 Predictive Model Development ¶

1.7.1 Model Application Programming Interface Code Development ¶

1.7.2 User Interface Application Code Development ¶

1.7.3 Web Application ¶

2. Summary ¶

3. References ¶

  • [Book] Deep Learning with Python by Francois Chollet
  • [Book] Deep Learning: A Visual Approach by Andrew Glassner
  • [Book] Learning Deep Learning by Magnus Ekman
  • [Book] Practical Deep Learning by Ronald Kneusel
  • [Book] Deep Learning with Tensorflow and Keras by Amita Kapoor, Antonio Gulli and Sujit Pal
  • [Book] Deep Learning by John Kelleher
  • [Book] Generative Deep Learning by David Foster
  • [Book] Deep Learning Illustrated by John Krohn, Grant Beyleveld and Aglae Bassens
  • [Book] Neural Networks and Deep Learning by Charu Aggarwal
  • [Book] Grokking Deep Learning by Andrew Trask
  • [Book] Deep Learning with Pytorch by Eli Stevens, Luca Antiga and Thomas Viehmann
  • [Book] Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
  • [Book] Deep Learning from Scratch by Seth Weidman
  • [Book] Fundamentals of Deep Learning by Nithin Buduma, Nikhil Buduma and Joe Papa
  • [Book] Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurelien Geron
  • [Book] Deep Learning for Computer Vision by Jason Brownlee
  • [Python Library API] numpy by NumPy Team
  • [Python Library API] pandas by Pandas Team
  • [Python Library API] seaborn by Seaborn Team
  • [Python Library API] matplotlib.pyplot by MatPlotLib Team
  • [Python Library API] matplotlib.image by MatPlotLib Team
  • [Python Library API] matplotlib.offsetbox by MatPlotLib Team
  • [Python Library API] tensorflow by TensorFlow Team
  • [Python Library API] keras by Keras Team
  • [Python Library API] pil by Pillow Team
  • [Python Library API] glob by glob Team
  • [Python Library API] cv2 by OpenCV Team
  • [Python Library API] os by os Team
  • [Python Library API] random by random Team
  • [Python Library API] keras.models by TensorFlow Team
  • [Python Library API] keras.layers by TensorFlow Team
  • [Python Library API] keras.wrappers by TensorFlow Team
  • [Python Library API] keras.utils by TensorFlow Team
  • [Python Library API] keras.optimizers by TensorFlow Team
  • [Python Library API] keras.preprocessing.image by TensorFlow Team
  • [Python Library API] keras.callbacks by TensorFlow Team
  • [Python Library API] keras.metrics by TensorFlow Team
  • [Python Library API] sklearn.metrics by Scikit-Learn Team
  • [Article] Convolutional Neural Networks, Explained by Mayank Mishra (Towards Data Science)
  • [Article] A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way by Sumit Saha (Towards Data Science)
  • [Article] Understanding Convolutional Neural Networks: A Beginner’s Journey into the Architecture by Afaque Umer (Medium)
  • [Article] Introduction to Convolutional Neural Networks (CNN) by Manav Mandal (Analytics Vidhya)
  • [Article] What Are Convolutional Neural Networks? by IBM Team (IBM)
  • [Article] What is CNN? A 5 Year Old guide to Convolutional Neural Network by William Ong (Medium)
  • [Article] Convolutional Neural Network by Thomas Wood (DeepAI.Org)
  • [Article] How Do Convolutional Layers Work in Deep Learning Neural Networks? by Jason Brownlee (Machine Learning Mastery)
  • [Article] Convolutional Neural Networks Explained: Using PyTorch to Understand CNNs by Vihar Kurama (BuiltIn)
  • [Article] Convolutional Neural Networks Cheatsheet by Afshine Amidi and Shervine Amidi (Stanford University)
  • [Article] An Intuitive Explanation of Convolutional Neural Networks by Ujjwal Karn (The Data Science Blog)
  • [Article] Convolutional Neural Network by NVIDIA Team (NVIDIA)
  • [Article] Convolutional Neural Networks (CNN) Overview by Nikolaj Buhl (Encord)
  • [Article] Understanding Convolutional Neural Network (CNN): A Complete Guide by LearnOpenCV Team (LearnOpenCV)
  • [Article] Convolutional Neural Networks (CNNs) and Layer Types by Adrian Rosebrock (PyImageSearch)
  • [Article] How Convolutional Neural Networks See The World by Francois Chollet (The Keras Blog)
  • [Article] What Is a Convolutional Neural Network? by MathWorks Team (MathWorks)
  • [Article] Grad-CAM Class Activation Visualization by Francois Chollet (Keras.IO)
  • [Article] Grad-CAM: Visualize Class Activation Maps with Keras, TensorFlow, and Deep Learning by Adrian Rosebrock (PyImageSearch)
  • [Kaggle Project] glioma 19 Radiography Data - EDA and CNN Model by Juliana Negrini De Araujo (Kaggle)
  • [Kaggle Project] Pneumonia Detection using CNN (92.6% Accuracy) by Madhav Mathur (Kaggle)
  • [Kaggle Project] glioma Detection from CXR Using Explainable CNN by Manu Siddhartha (Kaggle)
  • [Kaggle Project] Class Activation Mapping for glioma-19 CNN by Amy Zhang (Kaggle)
  • [Kaggle Project] CNN mri glioma Classification by Gabriel Mino (Kaggle)
  • [Kaggle Project] Detecting-glioma-19-Images | CNN by Felipe Oliveira (Kaggle)
  • [Kaggle Project] Detection of glioma Positive Cases using DL by Sana Shaikh (Kaggle)
  • [Kaggle Project] Deep Learning and Transfer Learning on glioma-19 by Digvijay Yadav (Kaggle)
  • [Kaggle Project] X-ray Detecting Using CNN by Shivan Kumar (Kaggle)
  • [Kaggle Project] Classification of glioma-19 using CNN by Islam Selim (Kaggle)
  • [Kaggle Project] glioma-19 - Revisiting Pneumonia Detection by Paulo Breviglieri (Kaggle)
  • [Kaggle Project] Multi-Class X-ray glioma19 Classification-94% Accurary by Quadeer Shaikh (Kaggle)
  • [Kaggle Project] Grad-CAM: What Do CNNs See? by Derrel Souza (Kaggle)
  • [GitHub Project] Grad-CAM by Ismail Uddin (GitHub)
  • [Publication] Gradient-Based Learning Applied to Document Recognition by Yann LeCun, Leon Bottou, Yoshua Bengio and Patrick Haffner (Proceedings of the IEEE)
  • [Publication] Learning Deep Features for Discriminative Localization by Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva and Antonio Torralba (Computer Vision and Pattern Recognition)
  • [Publication] Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization by Ramprasaath Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh and Dhruv Batra (Computer Vision and Pattern Recognition)
  • [Course] IBM Data Analyst Professional Certificate by IBM Team (Coursera)
  • [Course] IBM Data Science Professional Certificate by IBM Team (Coursera)
  • [Course] IBM Machine Learning Professional Certificate by IBM Team (Coursera)
In [109]:
from IPython.display import display, HTML
display(HTML("<style>.rendered_html { font-size: 15px; font-family: 'Trebuchet MS'; }</style>"))